Evaluation of an X-ray image of a breast produced during a mammography

ABSTRACT

The embodiments relate to a method, an apparatus, and a computer program for evaluating an x-ray image of a breast produced during a mammography. In order to simplify the evaluation of such an x-ray image in respect of the breast density, a method is proposed to automatically determine the masking risk caused by the mammographically dense tissue and to use this for categorizing, describing, and/or representing the breast density.

The present patent document is continuation of U.S. patent applicationSer. No. 15/308,135, filed Nov. 1, 2016, which is hereby incorporated byreference, and which is a § 371 nationalization of PCT ApplicationSerial Number PCT/EP2015/054429, filed Mar. 3, 2015, designating theUnited States, which is also hereby incorporated by reference, and thispatent document also claims the benefit of DE 10 2014 208 411.3, filedMay 6, 2014, which is also hereby incorporated by reference.

TECHNICAL FIELD

The embodiments relate to a method, an apparatus, and a computer programfor evaluating an x-ray image of a breast produced during mammography.

BACKGROUND

Automatically calculating the volumetric breast density (VBD) from anx-ray image produced during mammography is known. The volumetric breastdensity is defined as the ratio of the volume of the fibroglandulartissue to the overall volume of the breast. Below, the terms“fibroglandular tissue,” “glandular tissue,” and “mammographically densetissue” or “dense tissue” are used synonymously. On the basis of thisVBD value, the breast has until now been assigned a specific breastdensity category using fixed thresholds, e.g., a BI-RADS value from “1”to “4” according to the classification by the American College ofRadiology (ACR). By way of example, this is described in U.S. PatentPublication No. 2011/0026791 A1 and in DE 10 2006 021 042 A1.

Women whose breast has a high VBD value have an increased risk ofgetting breast cancer. The increase in this risk is partly traced backto the fact that cancerous tissue is masked by mammographically densetissue and therefore it is not identified during mammography.

It is known that the masking risk does not always correlate with the VBDvalue. FIGS. 1 and 2 depict a breast 3 compressed between two plates 1,2 during mammography. After passing through the tissue of the breast 3,x-ray radiation 5 emanating from an x-ray source 4 is incident on anx-ray detector 6. As depicted in FIGS. 1 and 2, the same volume offibroglandular tissue 7, 8 may cover small masses 9 in different ways.In the example depicted in FIG. 1, the fibroglandular tissue 7, whichhas a specific volume, is localized at a single position such that thesmall mass 9 is covered by the dense tissue 7. As a result of the volumeof the fibroglandular tissue 7, the depicted region of the breast 3 ischaracterized by a specific VBD value. By contrast, the fibroglandulartissue 8, which has an identical volume, is distributed more uniformlyin the volume of the breast 3 in the example depicted in FIG. 2, and soit is less likely for the small mass 9 to be covered by the dense tissue8. Despite the VBD values being identical, the risk of masking of thesmall mass 9 is lower in FIG. 2. Therefore, the sole use of the VBDvalue is not sufficient for an accurate description of the maskingeffect by mammographically dense tissue 7, 8.

It is for this reason that the 5th edition of the ACR BI-RADS Atlasproposes new categories “a” to “d” with a verbal description of thebreast density category, defined by the visually estimated portion offibroglandular tissue in the breast. Taking into account the maskingrisk was proposed for the first time in this context. Thus, the category“c” may be assigned if small masses may be obscured as a result of aheterogeneous density distribution. It is proposed that the radiologistin such a case describes the position of the dense tissue in a furthersentence.

Moreover, the nature of the breast may be described with the category“a” if the breasts are almost entirely fatty. The category “b” may bepresent if there are scattered areas of fibroglandular density. Thecategory “d” may be assigned if the breasts are extremely dense, whichlowers the sensitivity of mammography.

As depicted in FIG. 3, a glandularity map was previously calculated inact 101 using the x-ray image produced during mammography, with theglandularity denoting the proportion of the fibroglandular tissue in theoverall tissue. The glandularity map defines the amount of glandulartissue in each image pixel of the x-ray image, either as a specificationin millimeters or as a percentage specification, with the value lying ina range from, e.g., 1% to 50%. Then, the mean glandularity, the VBDvalue, is established in act 102. Subsequently, the categories “a” to“d” are determined in act 103 based purely on the VBD value. The actsare carried out either manually by a radiologist or already in anautomated form with the aid of available systems. However, onlyexperienced radiologists may undertake a reliable assessment of themasking risk and generate a complete breast density report, taking intoaccount the masking risk, in accordance with the prescriptions of theACR. Here, there is a risk of errors of judgment.

SUMMARY AND DESCRIPTION

The scope of the present invention is defined solely by the appendedclaims and is not affected to any degree by the statements within thissummary. The present embodiments may obviate one or more of thedrawbacks or limitations in the related art.

It is therefore an object to enable a more precise evaluation of anx-ray image of a breast, produced during mammography, in respect of thebreast density. This object is achieved by a method, an apparatus, or acomputer program described herein. The advantages and embodimentsexplained below in conjunction with the method apply analogously to theapparatus as well, and vice versa.

The embodiments proceed from taking the masking properties of thefibroglandular tissue into account to a greater extent when determiningthe breast density. A core concept lies in undertaking the evaluation ofthe x-ray image taking into account the masking risk and undertaking itin a largely, (e.g., completely), automated manner. To this end, themasking risk caused by mammographically dense tissue is determinedautomatically and used for categorizing, describing and/or representingthe breast density. A clinically applicable, standardized method forsimple and improved description of the breast density is provided by theautomatic quantification of the masking risk.

In one embodiment, a masking risk map for the region of the breastdepicted in the x-ray image is produced automatically to this end. Onthe basis thereof, a masking risk value (M value) may be producedautomatically, the masking risk value quantifying the masking risk forthe region of the breast depicted in the x-ray image. Advantageously,this M value is used in conjunction with the VBD value already usedpreviously in order to determine a breast density category in accordancewith the 5th edition of BI-RADS in a more precise manner.

In one embodiment, the produced masking risk map is displayed on ascreen together with the x-ray image in order to display those regionsof the image for which an increased masking risk was established. Aradiologist may use the information in respect of the masking risk inorder to concentrate on these regions during the evaluation of the x-rayimage.

A description of the position and/or the distribution ofmammographically dense tissue for the region of the breast depicted inthe x-ray image is produced automatically on the basis of the maskingrisk map in an embodiment, advantageously in the form of a shortsentence as may be used as part of the breast density report inaccordance with the 5th edition of BI-RADS.

It is particularly advantageous if some or all of the applicationsspecified above are combined with one another.

As disclosed herein, it is possible, for the first time, to generate aquantified, automatically generated breast density report taking intoaccount the masking risk, which breast density report meets therequirements of the 5th edition of BI-RADS. The accuracy of theclassification into the breast density categories to be undertaken isincreased in relation to conventional solutions. Subjective influencesand errors are reduced or precluded. The evaluation is carried out in aconsistent and reproducible manner. Overall, what is achieved is a verymuch simpler evaluation of an x-ray image of the breast, produced duringmammography, in respect to the breast density.

The embodiments are applicable in a particularly advantageous manner inthe case of digital mammography devices and in the field ofimage-assisted, e.g., automated breast density measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-described properties, features and advantages, and the mannerin which they are achieved, become more clear and more easilyunderstandable in conjunction with the following description ofexemplary embodiments, which are explained in more detail in conjunctionwith the drawings.

FIGS. 1 and 2 depict an illustration of the masking effect of relativelysmall masses by differently arranged fibroglandular tissue.

FIG. 3 depicts the conventional procedure when generating a breastdensity report (prior art).

FIG. 4 depicts the procedure when generating a breast density report inaccordance with an example.

FIG. 5 depicts a glandularity map of a breast according to an example.

FIG. 6 depicts an image of a breast after a threshold treatmentaccording to an example.

FIG. 7 depicts an image of a breast after a morphological openingaccording to an example.

FIG. 8 depicts a representation of a mapping function f, as is used forcalculating the M value, according to an example.

FIG. 9 depicts a masking risk map according to an example.

FIG. 10 depicts a representation of the VBD values in relation to breastdensity categories according to an example.

FIG. 11 depicts an apparatus according to one example.

All of the figures depict the embodiments in a schematic manner. Here,the same reference signs correspond to elements with the same or acomparable function.

DETAILED DESCRIPTION

The masking risk caused by mammographically dense tissue 7, 8 isdetermined automatically and used for categorizing, describing and/orrepresenting the breast density in the method for evaluating an x-rayimage 10 of a breast 3 produced during mammography. The x-ray image 10to be evaluated may be a full-field mammography (FFDM) image or aprojection image from a digital breast tomosynthesis (DBT) record.

If reference is made below to an “x-ray image”, this need not relate tothe whole record. Instead, this may also only relate to a currentlydisplayed or processed image region of the recorded x-ray image.

The basis of the method is the automatic production of a masking riskmap 11 for the region of the breast 3 depicted in the x-ray image; seeFIG. 4, act 104. This masking risk map 11 depicts the interconnected,dense tissue. Below, an exemplary way for producing such a masking riskmap 11 is described.

The data from a glandularity map 12 (G(x,y)) as depicted in an exemplarymanner in FIG. 5 serves as input data for establishing the masking riskmap 11. In the subsequent description, the assumption is made that theglandularity is specified in percent. A conversion of the glandularityinto millimeter (mm) units may easily be carried out by multiplying thepercentage by the known thickness of the compressed breast, measured inmm. Furthermore, the assumption is made that structures that are notrelevant to calculating the breast density, such as the pectoral muscleor the mammilla, were segmented and removed from the glandularity map12.

There are various options, known to a person skilled in the art, forcalculating such a glandularity map 12. The manner of the calculation ofthe glandularities is not a component disclosed herein. Instead, thepresent embodiments relate to the evaluation of the glandularity map 12,as described in more detail below.

When calculating the masking risk map 11, the glandularity map 12 isprocessed a number of times at different threshold planes in thesub-acts 104 a, 104 b and 104 c of act 104 (not depicted individually inFIG. 4). The number of run-throughs depends in this case on the numberof desired threshold planes. By way of example, a first threshold planeincludes breast density values between 10% and 15% and a secondthreshold plane includes breast density values between 15% and 20%, etc.Here, the threshold planes may overlap. In the subsequent example, thefirst threshold plane includes breast density values above 15%, thesecond threshold plane includes breast density values above 20%, and thethird threshold plane includes breast density values above 25% (N=3).The sub-acts 104 a, 104 b and 104 c are carried out N times (i=1, . . ., N), once for each threshold plane.

In the first sub-act 104 a, there is a binary segmentation into aforeground and background region. Here, the binary image I_(i)^(sg)(x,y) is produced by a threshold treatment of the glandularity map12 with the threshold T_(i)={15%; 20%; 25%}. Here, the assumption ismade that the T-values are sorted in an increasing sequence, e.g., thethreshold planes represent regions with increasing density. Hence, allimage pixels, in which the density is greater than the given thresholdT_(i), are marked in the produced image I_(i) ^(sg)(x,y). At thelocations of these image pixels, there is a potential risk of smallmasses 9 being obscured. FIG. 6 depicts an image I₁ ^(sg)(x,y) in anexemplary manner.

However, until now the connectivity of the dense tissue remainedunaccounted for. This is of importance here since a small isolatedregion of dense tissue with a glandularity greater than or equal to thethreshold T_(i) may be segmented in the employed approach of thethreshold treatment; nevertheless, it is in fact too small to actuallyobscure a small mass 9. The connectivity of the dense tissue istherefore also taken into account during the production of the maskingrisk map 11. This is carried out by virtue of a morphological openingmethod being applied in the sub-act 104 b following this. Here, theimage I_(i) ^(op)(x,y) is calculated by morphological opening of theimage I_(i) ^(sg)(x,y) with a disk-shaped structure element with theradius R_(i). By way of this sub-act, comparatively small isolatedregions of dense tissue, in which it is unlikely that they obscure smallmasses 9 on account of the size thereof, are removed. Here, adisk-shaped structure element is used since the assumption is made thatthe masses 9 primarily have a round form. FIG. 7 depicts an image I₁^(op)(x,y) in an exemplary manner.

The application of this sub-act is also of particular advantage becausethis also allows small, isolated, contrast-rich objects, such as e.g.microcalcification or metal clips, which may be contained in theglandularity map 12, to be removed.

It is particularly advantageous if, during the morphological openingmethod, the same structure elements are used for the removal and for therenewed addition, as this is already implemented as a standard method insoftware libraries. In the example, the radius R_(i)={0.4 cm; 0.4 cm;0.4 cm}. It is likewise possible to use disk-shaped structure elementswith different radii for the removal and the addition. It isparticularly advantageous if the radius for the addition is less thanthat for removal because then the final area is slightly smaller thanthe original area. As a result, it is possible to remove regions inwhich some of the mass lies in a dense region but another part of themass lies outside of the dense region, and so there merely is the riskof less-critical partial masking. Advantageously, a structure element isused in this context for the addition, the radius of which correspondsto a certain fraction of the radius of that structure element used forthe removal. It was found to be particularly advantageous if the radiusof the structure element used for the addition corresponds to a value of70% to 80% of the radius of the structure element used for the removal.

In the sub-act 104 c, the area A_(i) of the remaining foreground regionin the image I_(i) ^(op)(x,y) is calculated, e.g., the area of thatregion having such image pixels in which the density is greater than therespective threshold. This area A_(i) (with units of cm²) is a measurefor the risk for the respective threshold plane that the dense tissuecovers a small mass 9. By way of example the area depicted in FIG. 7 isA₁=13.5 cm². The further areas are, e.g., A₂=6.3 cm² and A₃=3.1 cm².

Using the obtained images I_(i) ^(op)(x,y) with i=1, . . . , N, thetwo-dimensional masking risk map 11 is generated in sub-act 104 d of theact 104 (which is likewise not depicted in detail in FIG. 4) afterrunning through the sub-acts 104 a to 104 c repeatedly, in which maskingrisk map the largest possible value i from the image set I_(i)^(op)(x,y), at which the image pixel is still segmented, e.g.,classified as foreground region, is assigned to each image pixel (x,y).An example for such a masking risk map 11 is depicted in FIG. 9. On thebreast depicted in dark blue, the glandularity map 12 of which isdepicted in FIG. 5, dense breast tissue 13, which remained unconsideredduring the evaluation of the masking risk due to the restrictedconnectivity thereof, is depicted in light blue. Dense tissue 14 with aglandularity above 15% is depicted in yellow, dense tissue 15 with aglandularity above 20% is depicted in orange and dense tissue 16 with aglandularity above 25% is depicted in red. A different color coding maybe used.

Subsequently, a masking risk value (M value) 17 is automaticallyproduced for the region of the breast depicted in the x-ray image 10;see FIG. 4, act 105. This M value 17 quantifies the masking risk forthis region of the breast 3. Here, a low M value 17 means that theprobability of a mass 9 being masked is low, while a high M value 17means a high masking probability. By way of example, in the case of ahigh M value 17, the radiologist may recommend carrying out a furtherexamination, for example, a breast ultrasound examination or a breasttomosynthesis. An exemplary way for producing such an M value 17 isdescribed below.

In a first sub-act 105 a, the calculated areas A_(i) are used tocalculate a (dimensionless) overall risk value (ρ) 18. By way ofexample, this is carried out by applying a linear risk model:

$\rho = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{c_{i}A_{i}}}}$where the coefficients c_(i) have increasing (or at least notdecreasing) values, which are c_(i)={2 cm⁻²; 5 cm⁻²; 8 cm⁻²} in theexample. As a result of this weighted summation, the areas with a higherdensity contribute more strongly to the overall risk. In the exampledescribed here, ρ=2.52. Instead of a linear risk model, it is alsopossible to use other, e.g., nonlinear models.

Since the interpretation of the overall risk value ρ is easier if thevalue is mapped to a numerical scale from 0% to 100%, there is anormalization in the subsequent sub-act 105 b using the mapping function(f) 21, as a result of which the masking risk value (M) 17 is generated.Here, M=f(ρ) applies.

The mapping function 21 may be a nonlinear function f(ρ)=1−exp(−αp),where α=0.57, as depicted in FIG. 8. An advantage of such a nonlinearfunction is that the behavior thereof is approximately piecewise linear.Here, the function is increasing in such a way that the increasing ρvalues lead to ever larger M values, and the function asymptoticallyreaches 100%. However, it is also possible to use other mappingfunctions f. In the example described here, M=74%.

Subsequently, the M value 17 is used to assign a breast density category20 to the region of the breast depicted in the x-ray image 10; see FIG.4, act 103. The assignment may be carried out in a fully automatedmanner. In another embodiment, the assignment also takes place in asemi-automated manner. This means that a proposition is automaticallypresented to the radiologist, which the radiologist may agree with orwhich the radiologist may modify.

The assignment of a breast density category 20 to the x-ray image 10 iscarried out using and by linking the VBD value 19, which may lie in arange between 2% and 30%, and the M value 17, which may assume a valuebetween 0% and 100%. Since the assumption may be made that low VBDvalues 19 lead to low M values 17 and high VBD values 19 lead to high Mvalues 17, it is expedient to use the M value 17 when assessing the meanvalue range of the VBD values 19. Here, distinguishing between the twobreast density categories “b” and “c” is of particular importance sincethe two categories “c” and “d” each relate to “dense” breasts and anadditional examination is recommendable according to the currentguidelines.

Therefore, it is proposed to use the M value 17 in such a way that itonly influences the assignment of the breast density categories “b” and“c”. FIG. 10 illustrates the region 22 of the VBD value 19 in which theM value 17 may influence the classification into the individual breastdensity categories “a” to “d”. Here, instead of using a sharp VBDthreshold of e.g. 10% for distinguishing between adjacent categories “b”and “c”, as was conventional up until now, a flexible threshold isproposed for delimiting these two breast density categories 20. By wayof example, this flexible threshold may extend over a range 22 of VBDvalues 19 from 9.2% to 10.8%.

The table below clarifies, in an exemplary manner, the assignment of aspecific breast density category 20 in a manner dependent on therespective M value 17 in the case of virtually identical VBD values 19in this threshold region 22.

VBD value M value Breast density category 9.2% 88% c 9.4% 23% b 10.2%23% b 10.7% 54% c

The use of other decision rules is possible. It is also possible forother factors, such as, e.g., the age of the woman, to be included inthe classification.

At the same time as calculating the M value 17, a description of theposition and/or the spatial distribution of mammographically densetissue is generated automatically on the basis of the masking risk map11 for the region of the breast 3 depicted in the x-ray image 10, e.g.,the description is generated using the map; see FIG. 4, act 107. Thisrelates in particular to the spatial arrangement of those regions 14,15, 16 having a very high masking risk; see the regions 16 depicted inred in FIG. 9. The masking risk map 11 is automatically analyzed withthe aid of a suitable algorithm and a corresponding description in textform, for example in the form of a short sentence, is generated andadded to the breast density report 23. Here, a standardized form of averbal description is advantageous, for example using a subdivision ofthe image into quadrants.

A correct breast density report 23 in accordance with the prescriptionsfrom the ACR is generated in act 106 together with the breast densitycategories 20 determined in act 103 and the position descriptionsgenerated in act 107.

In parallel thereto, the masking risk map 11, see FIG. 9, is depictedfor the radiologist on a screen 24 together with the x-ray image 10; seeFIG. 4, act 108. The radiologist may use this information to, e.g., beable to better evaluate the automatically generated suggestion of abreast density category 20 in act 103.

Here, the masking risk map 11 may be depicted next to the (processed)FFDM image or the reconstructed DBT data record, or the masking risk map11 may be fused with the FFDM image or the DBT data record. By way ofexample, the FFDM image or the DBT data record may be converted into theHSV color space. In this color space, grayscale value information isstored in the “V” channel. The two other channels “H” and “S” serve toencode the masking risk map 11. By changing the values in these twochannels, it is possible in a simple manner to control the displaystrength of the masking risk map 11 as an overlay on the FFDM image orthe DBT data record. In this manner, the radiologist may particularlyeasily superimpose or mask the masking risk map 11 on/from the x-rayimage 10. However, a change between the two views in the case of thesuperposition of the images or maps may also be brought about in adifferent way.

The values of the parameters (N, T_(i), R_(i), c_(i)) described in thedescribed exemplary embodiment may be made dependent on otherparameters, for example on the compressed breast thickness or onirradiation parameters.

If the x-ray image is a DBT record, n=25 recorded images may be present,with, e.g., each projection image being evaluated individually.Likewise, the information obtained from all n projections may be used tocalculate a common masking risk map 11, a common VBD value 21, and acommon M value 17. Here, use may be made of, e.g., a (weighted)averaging with removal of the outliers.

The apparatus 25 is embodied to carry out the described method. Theapparatus may include a data processing unit 26, which is embodied tocarry out all acts related to the processing of data in accordance withthe method described here. The processing unit 26 may have a pluralityof functional modules, wherein each functional module is embodied tocarry out a specific function or a plurality of specific functions inaccordance with the described method. By way of example, the apparatus25 has a screen 24 for displaying the x-ray image 10 and/or the maskingrisk map 11. Suitable input and output devices are likewise provided,such as interfaces for entering the x-ray image 10 and for outputtingthe breast density report 23.

The functional modules may be hardware modules or software modules.Expressed differently, the embodiment, to the extent that the embodimentrelates to the processing unit 26, may be implemented in the form ofcomputer hardware or in the form of computer software or in acombination of hardware and software. To the extent that the embodimentis implemented in the form of software, e.g., as a computer program 27,all described functions are realized by computer program instructionswhen the computer program 27 is executed on a computer 26 with aprocessor. Here, the computer program instructions are implemented in amanner known per se in any programming language and may be provided tothe computer in any form, for example, in the form of data packetstransmitted over a computer network or in the form of a computer programproduct stored on a disk, CD-ROM or any other data medium.

Although the invention was illustrated more closely and described indetail by way of the exemplary embodiments, the invention is notrestricted to the disclosed examples and other variations may be derivedtherefrom by a person skilled in the art without departing from thescope of protection of the invention. It is therefore intended that theforegoing description be regarded as illustrative rather than limiting,and that it be understood that all equivalents and/or combinations ofembodiments are intended to be included in this description.

It is to be understood that the elements and features recited in theappended claims may be combined in different ways to produce new claimsthat likewise fall within the scope of the present invention. Thus,whereas the dependent claims appended below depend from only a singleindependent or dependent claim, it is to be understood that thesedependent claims may, alternatively, be made to depend in thealternative from any preceding or following claim, whether independentor dependent, and that such new combinations are to be understood asforming a part of the present specification.

The invention claimed is:
 1. A method for evaluating an x-ray image of abreast produced during mammography, the method comprising: automaticallyproducing, by a processor, a masking risk map for a region of the breastdepicted in the x-ray image, wherein the producing takes into account aconnectivity of dense breast tissue, wherein the masking risk mapcomprises masking risk values that quantify a masking risk, and whereinthe masking risk map further comprises one or more categorizing levelsof masking risk; automatically determining, by the processor, an overallmasking risk of the breast caused by the dense breast tissue; andgenerating a breast density report using the overall masking risk. 2.The method of claim 1, wherein the connectivity of the dense breasttissue is taken into account by applying a morphological opening method.3. The method of claim 2, wherein a structure element used within ascope of the morphological opening method is disk-shaped and radii ofstructure elements used for ablation and addition differ from oneanother.
 4. The method of claim 1, further comprising: automaticallyproducing the masking risk values based on the masking risk map.
 5. Themethod of claim 4, further comprising: using the masking risk values toassign breast density categories to regions of the breast depicted inthe x-ray image.
 6. The method of claim 5, wherein the breast densityreport comprises the assigned breast density categories for the regionsof the breast depicted in the x-ray image.
 7. The method of claim 5,further comprising: automatically producing a description of a position,a distribution, or both the position and the distribution of the densebreast tissue based on the masking risk map.
 8. The method of claim 7,wherein the breast density report comprises the assigned breast densitycategories for the regions of the breast depicted in the x-ray image andthe description of the position, the distribution, or both the positionand the distribution of the dense breast tissue.
 9. The method of claim1, further comprising: automatically producing a description of aposition, a distribution, or both the position and the distribution ofthe dense breast tissue based on the masking risk map.
 10. The method ofclaim 1, further comprising: displaying the masking risk map on a screentogether with the x-ray image.
 11. The method of claim 1, wherein datafrom a glandularity map is used in the producing of the masking riskmap.
 12. The method of claim 11, wherein the glandularity map isprocessed for a plurality of different threshold planes, and wherein abinary image is produced for each threshold plane, each binary imagebeing based on breast density values within the respective thresholdplane.
 13. The method of claim 1, wherein the breast density reportcomprises descriptions of the dense breast tissue and volumetric breastdensities determined for the region of the breast depicted in the x-rayimage.
 14. An apparatus for evaluating an x-ray image of a breastproduced during mammography, the apparatus comprising: a processorconfigured to: produce a masking risk map for a region of the breastdepicted in the x-ray image, wherein the producing takes into account aconnectivity of dense breast tissue, wherein the masking risk mapcomprises masking risk values that quantify a masking risk, and whereinthe masking risk map further comprises one or more categorizing levelsof masking risk; determine an overall masking risk of the breast causedby the dense breast tissue; and generate a breast density report usingthe overall masking risk.
 15. A non-transitory computer-readable storagemedium stored on a computer for evaluating an x-ray image of a breastproduced during mammography, the computer-readable storage mediumcomprising computer program instructions configured to, when executed onthe computer, cause the computer to at least perform: produce a maskingrisk map for a region of the breast depicted in the x-ray image, whereinthe producing takes into account a connectivity of dense breast tissue,wherein the masking risk map comprises masking risk values that quantifya masking risk, and wherein the masking risk map further comprises oneor more categorizing levels of masking risk; determine an overallmasking risk of the breast caused by the dense breast tissue; andgenerate a breast density report using the overall masking risk.